In recent years in China, the tense physician-patient relationship has been an outstanding problem. Empathy is one of the fundamental factors enhancing the therapeutic effects of physician-patient relationships and is significantly associated with clinical and academic performance among students.

Geographical accessibility is important for promoting health equity, and calculating it requires the locations of all existing healthcare facilities in a region. Authoritative location data collected by governments is accurate but mostly not publicly available, while point-of-interest (POI) data from online sources, such as Baidu Maps and AutoNavi Maps are easily accessible. However, the accuracy of the latter has not been thoroughly analyzed. Taking Baotou, a medium-sized city in China, as aneample, we assessed the suitability of using POI data for measuring geographic accessibility to healthcare facilities.We computedthe difference of geographic accessibility calculated based on POI data and that on authoritative data.Logistic regression and a multiple linear regression model was applied to identify factors related to the consistency between the two data sources. Compared to authoritative data, POI data exhibited discrepancies, with completeness of 54.9% and accuracy of 63.7%. Geographic accessibility calculated based on both data showed similar patterns, with good consistency for hospitals and in urban areas. However, large differences (>30 minutes) were shown in rural areas for primary healthcare facilities. The differences were small regarding to population- weighted average accessibility (with slight underestimation of 3.07 minutes) and population coverage across various levels of accessibility (with differences less than 1% of the population) for the entire area. In conclusion, POI data can be considered foruse in both urban areas and at the level of entire city; however, awareness should be raised in rural areas.

Objectives: To explore the prognostic value of eight inflammation-nutrition biomarkers in patients with lung cancer and tuberculosis as no multidimensional prognostic models for this comorbid population are available currently.Methodology: A retrospective study included 100 patients with lung cancer and tuberculosis admitted to a tertiary hospital from October 2019 to October 2024. Eight inflammation-nutrition markers (NLR, PLR, SII, LMR, PNI, HALP, HRR, ALB/GLB) were chosen as predictors while overall survival (OS) was the major event. Feature selection was implemented by LASSO regression; a Cox proportional hazards model was established afterwards. The nomogram’s performance was assessed by ROC curve and C-index as well as the calibration using bootstrap resampling. The statistical power was calculated by PowerSurvEpi and sensitivity analyses were implemented to test the robustness of the model.Results: There were six predictors remaining in the final model including diabetes, ECOG PS, NLR, PNI, HRR and RDW. Among them, ECOG PS was an independent prognostic factor (HR = 1.76, p = 0.04). The nomogram achieved a good performance (C-index = 0.71), an AUC of 0.693 for 3-year OS as well as an excellent calibration (Bootstrap P > 0.05). In the high-risk subgroup with ECOG PS ≥ 2 and NLR>8, the 5-year survival rate was close to zero. The model achieved an adequate statistical power (83%, α = 0.05). Sensitivity analysis revealed an significant interaction between ECOG PS and NLR (p = 0.032) and NLR>8 was the most robust threshold for this interaction.Conclusion: This is the first study to establish and validate a combined inflammation-nutrition prognostic model for patients with lung cancer and tuberculosis. Our model provides a quantitative tool to stratify individual risk and offers evidence for the usage of nutritional interventions in high-risk patients.

Background: In China, which has the world’s largest and one of the fastest-aging populations, multimorbidity has become a major public health challenge. Older individuals with multiple chronic conditions experience poor health outcomes, increased healthcare utilization, and substantial financial pressure. This study investigates of the financial burden on older inpatients with multimorbidity. Methods: Utilizing 2022 inpatient data from a Chinese city (covering 4 secondary and 2 tertiary hospitals), this study employed descriptive statistics (means, standard deviations, medians, and interquartile ranges), comparative tests (chi-square, t -test, ANOVA), and multiple linear regression to identify key drivers of medical costs and out-of-pocket expenses for older inpatients with multimorbidity. Results: The study revealed a high multimorbidity rate of 73.8% among 106,202 inpatients aged ≥50 years from six Chinese hospitals in 2022. The average total hospitalization cost was 10,318 yuan, with an out of pocket expense (OOPE) of 4,558 yuan. Descriptive analysis showed the largest patient proportion was aged 60–69 years (38.4%) and most had 2–4 chronic conditions. Analysis of medical expenses identified significant variations. Patients with urban employee insurance had higher total costs but lower OOPE (24.99% proportion) compared to those with urban–rural resident insurance (50.89% proportion). Treatment in tertiary hospitals and longer hospital stays were strongly associated with higher costs. Multiple regression confirmed hospital level, length of stay, and surgical intervention were the most significant positive predictors of total costs. For OOPE, health insurance type, hospital level, and length of stay were major influencing factors. Conclusion: Multimorbidity is highly prevalent and imposes a substantial financial burden on older inpatients. Strengthening prevention, optimizing insurance design, and improving integrated care are essential for promoting healthcare equity and sustainability in China.